Vegetation Segmentation in Cornfield Images Using Bag of Words

Campos, Yerania; Rodner, Erik; Denzler, Joachim; Sossa, Humberto; Pajares, Gonzalo

doi:10.1007/978-3-319-48680-2_18

Cited by 4 publications

(6 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In our case, the vegetation contains very irregular texture patterns and the precision results obtained with these two methods have been low. More advanced methods for the extraction of information in the neighborhood of a pixel are based on the detection of keypoints and the construction of invariant descriptors, among which SURF and SIFT stand out [22]. However, the construction of the descriptors is a costly process which must be done in multiple positions within each superpixel.…”

Section: Discussionmentioning

confidence: 99%

“…More elaborate texture methods based on local invariant descriptors, such as speeded-Up robust features (SURF) and scale-invariant feature transform (SIFT), can also be used for characterizing vegetation species [22,23]. A classification chain based on textures may be combined with other types of features obtained by UAVs to improve the classification results.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Watershed Monitoring in Galicia from UAV Multispectral Imagery Using Advanced Texture Methods

et al. 2021

View full text Add to dashboard Cite

Watershed management is the study of the relevant characteristics of a watershed aimed at the use and sustainable management of forests, land, and water. Watersheds can be threatened by deforestation, uncontrolled logging, changes in farming systems, overgrazing, road and track construction, pollution, and invasion of exotic plants. This article describes a procedure to automatically monitor the river basins of Galicia, Spain, using five-band multispectral images taken by an unmanned aerial vehicle and several image processing algorithms. The objective is to determine the state of the vegetation, especially the identification of areas occupied by invasive species, as well as the detection of man-made structures that occupy the river basin using multispectral images. Since the territory to be studied occupies extensive areas and the resulting images are large, techniques and algorithms have been selected for fast execution and efficient use of computational resources. These techniques include superpixel segmentation and the use of advanced texture methods. For each one of the stages of the method (segmentation, texture codebook generation, feature extraction, and classification), different algorithms have been evaluated in terms of speed and accuracy for the identification of vegetation and natural and artificial structures in the Galician riversides. The experimental results show that the proposed approach can achieve this goal with speed and precision.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Watershed Monitoring in Galicia from UAV Multispectral Imagery Using Advanced Texture Methods

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Few works in the literature proposed descriptor-based methods for the classification of vegetation in images, although this approach is common in the classification of scenes. For example, the authors of [18] present a methodology for vegetation segmentation in cornfield images obtained by autonomous agricultural vehicles. A collection of outdoor color images, which were acquired under different illumination conditions and different plant growth state, were selected.…”

Section: Discussionmentioning

confidence: 99%

“…More elaborate texture methods based on local invariant descriptors such as SURF and SIFT can also be used for characterizing vegetation species. For example, a methodology for vegetation segmentation in cornfield images obtained by UAVs is presented in [18]. Specifically, it focuses on finding an appropriate set of different color vegetation indices and local descriptors for vegetation characterization.…”

Section: Introductionmentioning

confidence: 99%

“…Most of the vegetation classification methods proposed in the literature use regular patches [6,8,10,12,13,15,16,19]. In other cases, segmentation or object detection algorithms for dividing the image into regions are used [14,17,18]. A technique commonly used for the extraction of uniform regions in images is the segmentation based on superpixels [23,24].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Texture Extraction Techniques for the Classification of Vegetation Species in Hyperspectral Imagery: Bag of Words Approach Based on Superpixels

2020

View full text Add to dashboard Cite

Texture information allows characterizing the regions of interest in a scene. It refers to the spatial organization of the fundamental microstructures in natural images. Texture extraction has been a challenging problem in the field of image processing for decades. In this paper, different techniques based on the classic Bag of Words (BoW) approach for solving the texture extraction problem in the case of hyperspectral images of the Earth surface are proposed. In all cases the texture extraction is performed inside regions of the scene called superpixels and the algorithms profit from the information available in all the bands of the image. The main contribution is the use of superpixel segmentation to obtain irregular patches from the images prior to texture extraction. Texture descriptors are extracted from each superpixel. Three schemes for texture extraction are proposed: codebook-based, descriptor-based, and spectral-enhanced descriptor-based. The first one is based on a codebook generator algorithm, while the other two include additional stages of keypoint detection and description. The evaluation is performed by analyzing the results of a supervised classification using Support Vector Machines (SVM), Random Forest (RF), and Extreme Learning Machines (ELM) after the texture extraction. The results show that the extraction of textures inside superpixels increases the accuracy of the obtained classification map. The proposed techniques are analyzed over different multi and hyperspectral datasets focusing on vegetation species identification. The best classification results for each image in terms of Overall Accuracy (OA) range from 81.07% to 93.77% for images taken at a river area in Galicia (Spain), and from 79.63% to 95.79% for a vast rural region in China with reasonable computation times.

show abstract

Segmentation of vegetation and microplots in aerial agriculture images: A survey

Mardanisamani

Eramian

2022

The Plant Phenome Journal

View full text Add to dashboard Cite

Because of the increasing global population, changing climate, and consumer demands for safe, environmentally friendly, and high‐quality food, plant breeders strive for higher yield cultivars by monitoring specific plant phenotypes. Developing new crop cultivars and monitoring through current methods is time‐consuming, sometimes subjective, and based on subsampling of microplots. High‐throughput phenotyping using unmanned aerial vehicle‐acquired aerial orthomosaic images of breeding trials improves and simplifies this labor‐intensive process. To perform per‐microplot phenotype analysis from such imagery, it is necessary to identify and localize individual microplots in the orthomosaics. This paper reviews the key concepts of recent studies and possible future developments regarding vegetation segmentation and microplot segmentation. The studies are presented in two main categories: (a) general vegetation segmentation using vegetation‐index‐based thresholding, learning‐based, and deep‐learning‐based methods; and (b) microplot segmentation based on machine learning and image processing methods. In this study, we performed a literature review to extract the algorithms that have been developed in vegetation and microplots segmentation studies. Based on our search criteria, we retrieved 92 relevant studies from five electronic databases. We investigated these selected studies carefully, summarized the methods, and provided some suggestions for future research.

show abstract

Vegetation Segmentation in Cornfield Images Using Bag of Words

Cited by 4 publications

References 31 publications

Watershed Monitoring in Galicia from UAV Multispectral Imagery Using Advanced Texture Methods

Watershed Monitoring in Galicia from UAV Multispectral Imagery Using Advanced Texture Methods

Texture Extraction Techniques for the Classification of Vegetation Species in Hyperspectral Imagery: Bag of Words Approach Based on Superpixels

Segmentation of vegetation and microplots in aerial agriculture images: A survey

Contact Info

Product

Resources

About